The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Finding clauses in unrestricted text by finitary and stochastic methods
ANLC '88 Proceedings of the second conference on Applied natural language processing
Optimizing syntax patterns for discovering protein-protein interactions
Proceedings of the 2005 ACM symposium on Applied computing
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
Text Mining for Biology And Biomedicine
Text Mining for Biology And Biomedicine
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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This paper describes a Biological Literature Miner (BioLMiner) system and its implementation. BioLMiner is a text mining system for biological literature, whose purpose is to extract useful information from biological literature, including gene and protein names, normalized gene and protein names, and protein-protein interaction pairs. BioLMiner has three main subsystems in a pipeline structure: a gene mention recognizer (GMRer), a gene normalizer (GNer), and a protein-protein interaction pair extractor (PPIEor). All these subsystems are developed based on the machine learning techniques including support vector machines (SVMs) and conditional random fields (CRFs) together with carefully designed informative features. At the same time, BioLMiner makes use of some biological specific resources and existing natural language processing tools. In order to evaluate and compare BioLMiner, it is adapted to participate in two tasks of the BioCreative II.5 challenge: interaction normalization task (INT) using GNer and interaction pair task (IPT) using PPIEor. Our system is among the highest performing systems on the two tasks from which it can be seen that GMRer provides a good support for the INT and IPT although its performance is not evaluated, and the methods developed in GNer and PPIEor are extended well to the BioCreative II.5 tasks.